For this purpose, in the literature of recommender systems, early studies mainly conduct cross-domain recommendation methods [1]}, [2]}, [3]} by transferring the learned knowledge from existing domains to a new one. They assume that shared information (e.g., overlapping users/items [4]}, [5]}, [1]} or common features [7]}) is available for learning cross-domain mapping relations. However, in real applications, users or items are only partially shared or completely non-overlapping across different domains (especially in a cross-platform setting), making it difficult to effectively conduct cross-domain transfer. Besides, previous content-based transfer methods [7]}, [9]} usually design specific approaches tailored for the data format of shared features, which is not generally useful in various recommendation scenarios.
